To develop miRNA FISH for tumor differential diagnosis, we selected a rare diagnostic problem in skin pathology involving basal cell carcinoma (BCC) and Merkel cell carcinoma (MCC), leveraging our preliminary findings that each tumor expresses cell lineage–specific miRNAs in high abundance. BCC and MCC frequently share overlapping histologic features and are typically differentiated from each other and similar appearing tumors on histologic grounds and/or using a panel of immunohistochemical stains (8–10). Although there is little specific need to optimize BCC and MCC diagnostics, this unusual scenario was ideal for our proof-of-concept study.

Identification of differentially expressed miRNAs in BCC and MCC tumor tissues and cell lines. To assess miRNA expression differences among tumors, we first extracted total RNA from 36 archived clinical materials and cultured cell lines from patients with BCC, MCC, and NS (Supplemental Table 1; supplemental material available online with this article; doi:
10.1172/JCI68760DS1). We subsequently profiled and quantitated miRNAs in all samples using our barcoded small RNA sequencing method (14). Sequence reads were annotated by RNA category (Supplemental Table 2). Total miRNA concentrations were calculated from sequence read frequencies of miRNAs relative to spike-in calibrator RNAs (Supplemental Table 3). Higher total miRNA concentrations were seen in TRIzol-extracted cell lines compared with those in MasterPure- or RecoverAll-extracted FFPE samples (Supplemental Figure 1). To minimize the effects of sample processing and/or RNA extraction method on RNA recovery (15), we compared concentrations in RecoverAll-extracted FFPE samples from sequencing run one (hereafter termed the training set). Total miRNA concentration was 2-fold higher in BCC than in MCC or NS, whereas no significant difference was seen between MCC and NS (Supplemental Figure 1). Tumor-specific differences in total miRNA concentration may reflect altered rates of miRNA biogenesis, processing, or degradation (2).

Unsupervised hierarchical clustering of miRNA expression profiles. Unsupervised hierarchical clustering was performed using log2 relative frequencies (RF) of miRNA precursor cluster sequence reads for the given cell line and FFPE tissue samples; MCV status is also indicated where available. miRNA precursor clusters were selected from the top 85% expressed miRNA precursor clusters across all samples. The number of members per precursor cluster is indicated in parentheses following the miRNA gene name; precursor clusters are named according to Farazi et al. (30). miR-205 and miR-375 expression values for all samples are indicated by red and green arrows, respectively.

We compared miRNA expression profiles between MCC and non-MCC groups and identified tumor-specific miRNAs through discriminant analysis. These groups were accurately differentiated based on miR-205 and miR-375 expression, with no errors in the training set (sequencing run one) and one misassignment (sample NS5a) in the testing set (sequencing run two). Significant differences in miR-205 and miR-375 concentrations were seen between MCC and non-MCC groups (ANOVA normalized MCC group = 20, normalized non-MCC group = 15); miR-205 concentrations were 4.5-fold higher in non-MCC groups than in MCC groups (adjusted P value [Padj] = 0.26), and miR-375 concentrations were 60-fold higher in MCC groups than in non-MCC groups (Padj < 0.001) (Supplemental Figure 2). Significant differences were again seen when analyzing training set samples only (ANOVA Padj < 0.001, normalized MCC = 12, normalized non-MCC = 8); miR-205 concentrations were 308-fold higher in non-MCC groups than in MCC groups (Padj < 0.001), whereas miR-375 concentrations were 577-fold higher in MCC groups than in non-MCC groups (Padj < 0.001) (Supplemental Figure 2). We confirmed grouping and expression differences in a subset of MCC and NS samples using miRNA microarray and real-time RT-PCR analyses (Supplemental Figure 2).

Addressing technical shortcomings in multicolor miRNA FISH. After identifying tumor-specific biomarkers for BCC (miR-205) and MCC (miR-375) in this proof-of-concept scenario, we established a multicolor miRNA FISH protocol suitable for use on FFPE tissue sections. To achieve this goal, we revisited RNA fixation, signal detection and amplification, and oligonucleotide probe design steps. Suboptimal RNA fixation leading to short and long RNA loss by diffusion, rather than RNA degradation, is the primary problem in optimizing RNA FISH (Supplemental Figure 3). To resolve this problem, we previously reported the use of 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide (EDC), a water-soluble condensation reagent that promotes phosphoamide bond formation between the miRNA 5′ phosphate end and aliphatic amines from amino acid side chains of surrounding proteins (Supplemental Figure 4A and ref. 16).

We further studied the EDC condensation reaction in vitro by reacting 5′ AMP and modified peptides. Near-complete phosphoamide bond formation was seen following incubation at pH 8 for 10 hours at 50°C (Supplemental Figure 5). We found that the phosphate moiety predominantly reacted with the primary amine of lysine (Supplemental Figure 6). We also observed a pH-dependent hydrolysis side reaction of the EDC-miRNA intermediate that reduced the yield of crosslinking between protein and miRNA (data not shown). To further investigate the condensation and side reactions, we prepared N-(4-aminobutyl) isonicotinamide (ABINA), a highly soluble UV-absorbing model compound with a pKa similar to that of the lysine side chain amine (Supplemental Figure 7). We subsequently optimized condensation reaction conditions by reacting ABINA with AMP (Supplemental Figure 8) in the presence of various EDC derivatives (Supplemental Table 4) and heterocyclic compounds (Supplemental Table 5) that form stable reaction intermediates and reduce hydrolysis. The reaction time was reduced to 1 hour using EDC-methiodide and 5-ethylthio-1H-tetrazole (5-ETT). The use of two heterocyclic derivatives (1-methylimidazole and 5-ETT) also resulted in competing intermediate formation, enhancing nucleophilic attack of the aliphatic amine and rapid crosslinking (Supplemental Figure 4B). We ultimately selected EDC-hydrochloride (EDC-HCl) and 5-ETT in 1-methylimidazole buffer for miRNA crosslinking, because the mixture is cheaper and more stable and the short reaction time (3 hours) minimizes miRNA diffusion during EDC fixation. miRNA retention in EDC-fixed FFPE tissues was confirmed in Northern blot analyses (Supplemental Figure 3).

In addition to miRNA crosslinking, EDC promoted protein crosslinking through the formation of carboxylic acid amide bonds. We modeled this reaction using ABINA and 3-pyridylacetic acid (Supplemental Figure 9). Complete amide bond formation was observed after 6 hours, double the completion time for phosphoamide bond formation under the same reaction conditions. To assess the effects of protein crosslinking on tissue sections, we monitored the retention of abundant long RNAs. Paraffin embedding of formalin-fixed tissues is accompanied by mild RNA hydrolysis and fragmentation of RNAs into smaller pieces; hydrolysis products carrying 2′,3′ cyclic and 2′ or 3′ phosphate termini do not yield stable phosphoamide bonds (17). We detected 28S rRNA using directly labeled fluorescent locked nucleic acid–modified (LNA-modified) oligodeoxynucleotide probes complementary to rRNA sequences. As expected, the rRNA signal was predominantly cytoplasmic with the exception of nucleolar staining, corresponding to the sites of rRNA biogenesis. EDC fixation also increased the retention of partially hydrolyzed rRNAs (Supplemental Figure 10). Monitoring rRNA enabled us to assess RNA fixation; confirm RNA retention, integrity, and specificity for probe hybridization; and normalize miRNA signals.

Following EDC treatment, increased fixation of the protein matrix was anticipated to hinder access of antibody-based signal amplification reagents to the target-RNA-bound probe-conjugated haptens. miRNA detection by directly labeled probes was not possible, because these small RNAs are at least 100-fold less abundant than rRNAs, and the rRNA signal obtained from one directly labeled LNA-modified probe was only 100-fold above background (data not shown). To enable access of the target-RNA-bound probe-conjugated hapten to detection by antibodies, we systematically varied the linker length between the nucleic acid probe and hapten. Using rRNA as a target, we compared a biotin-labeled oligonucleotide probe with varying linker lengths to a set of directly labeled fluorescent probes hybridizing at distinct sites; linker lengths above 10 nm substantially improved signal amplification–based fluorescence detection, presumably by increasing the fraction of haptens displayed at the surface of the tissue section (Supplemental Figure 11).

To amplify the hybridization signal of hapten-conjugated probes, we used tyramide signal amplification and enhanced the HRP-mediated oxidative tyramide coupling reaction by adding 4-bromophenylboronic acid (18). We confirmed optimal tyramide signal amplification for our reagent set by preparing Cy3-tyramide reagents and buffers for comparison with commercial Cy3-tyramide equivalents. After optimizing the reaction, we switched to tyramides of ATTO dyes that are brighter, more stable, and water soluble.

Direct visualization of rRNA by fluorescently labeled probes enabled us to assess probe specificity. Mishybridization of LNA-modified miRNA probes to rRNA was detected through colocalization of signals to nucleoli. During our initial studies, we noticed that the probes for miR-375, and for miR-205 to a lesser degree, yielded an rRNA-like pattern, indicating cross-hybridization (Supplemental Figures 12 and 13, respectively). Upon sequence analysis, we realized that 8-nt short segments of complementarity to rRNA, especially when modified by LNA residues, were responsible for probe mishybridization to rRNA, which could be corrected through probe shortening and placing LNAs outside segments (not longer than 6–7 nt) with rRNA sequence complementarity (Supplemental Figures 12 and 13 and Supplemental Table 6).

Differentiating BCC and MCC using normalized miRNA signal intensity ratios. Following signal collection and correction (see Supplemental Figure 15 and Supplemental Table 7), miR-205 (black) and miR-375 (gray) signal intensities were normalized against rRNA signal intensities for each tumor. A cutoff value (0.4; indicated by red line) to differentiate BCC and MCC was first established on a test set (BCC1 and MCC1) of tumors. This cutoff value was subsequently used in blinded analysis to correctly identify 4 BCC (BCC2-5) and 12 MCC (MCC2-13) tumors.

Multicolor RNA FISH for histologic differentiation also offers advantages in rapid design and high-throughput synthetic generation of nucleic acid probes, quantitative detection of both coding and noncoding transcripts, discrimination among mRNA isoforms, and multiplexing capacity. In contrast, immunohistochemical methods for histologic differentiation rely on costly diagnostic-grade antibodies and require many months for antibody generation, validation, and adaptation for antigen retrieval in fixed tissues. It is important to note that our miRNA FISH and conventional immunohistochemical methods are incompatible because EDC-based protein crosslinking alters antigen structure (Supplemental Figure 18); however, conventional H&E staining can be readily performed upon acquisition of fluorescent images (e.g., Figure 2).

Our long-term goal is to develop RNA FISH for molecular diagnostic purposes. Combining RNA sequencing and FISH technologies provides a powerful platform for rapidly identifying and visualizing disease-specific biomarkers. Although we focused on two miRNAs in two skin cancers for this proof-of-principle study, we expect our method to be widely applicable, because EDC fixation pertains to all miRNAs in any FFPE tissue sample, probe design (typically aiming for a temperature of 60°C in 50% formamide by varying probe length and the number of incorporated LNAs) and signal detection and amplification steps are flexible, and experimental conditions (i.e., 50% formamide for complete duplex denaturation, fixed hybridization temperature at 50°C) are constant. Our next step is to explore the clinical utility of RNA FISH in larger sample collections. Although the present proof-of-concept study is small, it demonstrates the potential and relative ease of implementing RNA FISH in clinical laboratories.

Aliquots of 6 MCC-derived cell lines were obtained from the Tumor Virology Laboratory, University of Pittsburgh Cancer Institute. MCV status was established using PCR, Southern blot, and immunohistochemical stains for MCV T antigen (24).

RNA extraction and RNA integrity assessment. Total RNA was extracted from FFPE tissue punches and rolls, respectively, using the RecoverAll Total Nucleic Acid Isolation Kit (Ambion) or the MasterPure Complete DNA and RNA Purification Kit (Epicenter Biotechnologies) according to the manufacturer’s guidelines. Total RNA was extracted from cultured cell lines using TRIzol Reagent (Invitrogen) following the manufacturer’s instructions. Total RNA concentrations were determined using a Nanodrop spectrophotometer ND-1000 or a Bio-Rad SmartSpec Plus Spectrophotometer. Concentrations of recovered total RNA ranged from 0.28 to 2.44 μg/μl, 0.01 to 1.80 μg/μl, and 0.81 to 3.65 μg/μl for RecoverAll, MasterPure, and TRIzol methods, respectively. Low RNA yields for samples NS5a and NS5b likely resulted from difficulty in dissolving paraffin from tissue rolls. RNA integrity was assessed by visual inspection of 28S and 18S rRNA bands following electrophoresis of 1 μg total RNA on an agarose gel stained with ethidium bromide.

Sequencing-based miRNA expression profiling, quantitation, and sample clustering. Barcoded small RNA sequencing was performed as described previously (14); total RNA input was 2 μg per sample, except where limited sample (NS5a, NS5b) was available. Barcoded sequence reads were annotated as reported previously (25). We excluded 1 sample (MCC14b) from further analyses due to the low (<20,000) number of miRNA sequence reads. miRNA expression profiles were generated from relative counts of different miRNAs within a sample. Total miRNA concentrations were calculated for each sample by multiplying the ratios of miRNA to calibrator read frequencies by the amount of input calibration marker per μg of total RNA input. Hierarchical clustering was performed as described previously (25), and miRNAs were presented as precursor clusters according to our ongoing human miRNA reannotation studies (26). The sequencing data discussed in this publication have been deposited in the NCBI’s Gene Expression Omnibus (accessible through GEO series accession no. GSE34137;
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34137).

Microarray-based miRNA expression profiling and data analysis. We profiled miRNA expression in FFPE tissues from 6 MCC and 2 NS samples using Agilent human miRNA arrays (27). Briefly, 100 ng total RNA was dephosphorylated, ligated to pCp-Cy3 using T4 RNA ligase 1, purified, and hybridized to an Agilent Human miRNA Microarray (v2), consisting of 8 identical subarrays with probes for 723 human miRNAs. Following scanning with the Agilent microarray scanner (Agilent), images were acquired and analyzed using Agilent feature extraction software version 9.5.3. GeneSpring software was used to normalize and log transform raw data and perform unsupervised hierarchical clustering. Differential expression between MCC and non-MCC groups was assessed by an unpaired t test. P values were adjusted for multiplicity using the Benjamini-Hochberg approach to control the false discovery rate. miRNAs were considered differentially expressed if the false discovery rate was <0.05 and the fold change was ≥2. The array data discussed in this publication have been deposited in the NCBI’s Gene Expression Omnibus (accessible through GEO series accession no. GSE-45146;
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45146).

miRNA real-time PCR. For comparison with sequencing-derived miRNA concentration estimates, we measured miR-375 expression in total RNA extracted from FFPE tissue punches from 11 MCC and 4 NS samples using TaqMan MicroRNA Assays (Applied Biosystems) according to the manufacturer’s guidelines; miR-205 expression was not studied due to a paucity of material. Briefly, miRNAs were reversed transcribed using miRNA-specific stem-loop RT primers (Applied Biosystems). Subsequently, real-time PCR reactions were performed using the Eppendorf Realplex System (Eppendorf). PCR reactions were incubated in a 96-well plate at 95°C for 10 minutes, followed by 40 cycles at 95°C for 15 seconds and 60°C for 1 minute. All samples were assayed in triplicate, and data were normalized to endogenous RNU6B. Relative miRNA expression levels were calculated using the ΔΔCt method.

Tissue sectioning. FFPE tissue blocks were cooled on ice prior to sectioning; 5- or 20-μm tissue sections were cut using a Leica RM2255 Rotary Microtome. For mounting sections on glass slides, sliced tissues were manually unfolded in a room temperature water bath and then transferred to a 42°C water bath to allow tissue stretching. To minimizing miRNA diffusion, incubation times at 42°C were less than 10 seconds. Tissue sections were immediately mounted on Colorfrost Plus slides (Fisher Scientific, catalog no. 12-550-18), air dried for 1 hour, and incubated in an oven at 56°C for 1 hour to attach the tissue slice to the slide upon melting of the paraffin.

Antisense LNA-modified oligodeoxynucleotide probes targeting miR-205 and miR-375 were designed using mature miRNA sequences from miRBase (http:/
www.mirbase.org). To minimize rRNA cross-hybridization, probe sequences with greater than 6 consecutive nucleotide matches were avoided and shortened to 14 nt and 15 nt for miR-205 and miR-375, respectively (Supplemental Table 6). Hairpin formation and self-dimerization of probe sequences were predicted using MFold (
http://mfold.rna.albany.edu), and LNA modifications were placed in regions with no secondary structure or self-hybridization. LNA probes were synthesized at 1.0-μmol scale on an ABI 3400 DNA synthesizer, deprotected, and quantified, and their UV profiles were determined as previously published (16).

Following synthesis, the CPG was transferred to a 1.5 ml screw cap tube and incubated with 1.2 ml 28%–30% aqueous ammonium hydroxide solution (EMD, catalog no. AX1303-6) for 16 hours at 55°C. The tube was placed on ice for 5 minutes, and the supernatant was transferred to a 13-ml centrifugation tube. Ten milliliters 1-butanol was added and vigorously mixed, and the LNA pellet was collected by centrifugation in a Sorvall RC5C Plus centrifuge (SS-34 rotor) at 20,000 g at 4°C for 20 minutes. The supernatant was removed completely, and the pellet was dried in an Eppendorf Vacufuge concentrator and redissolved in 400 μl water. After deprotection and precipitation, probes were directly used in miRNA FISH approach, without further denaturing PAGE gel purification.

Microscopy, image processing, and signal normalization. Images in Figure 2 and Supplemental Figures 10, 11, 16, and 17 were captured using the Olympus VS110 Virtual Microscopy System using ×20 and ×60 UPlanSApo objectives. For fluorescent imaging, we used ATTO dye combinations and the 86000v2 Sedat Quad filter set (Chroma), including filters for DAPI, Cy2, Cy3, and Cy5 (Supplemental Table 8). Images in Supplemental Figures 12 and 13 were captured on an Olympus BX50 microscope equipped with a DP70 camera and Olympus DP controller software. For fluorescent imaging, we used the following filter sets: U-MWU2 (Olympus) for DAPI, 41001 HQ (Chroma) for Alexa Fluor 488, 49004 ET (Chroma) for Cy3, and 49006 ET (Chroma) for Cy5.

Coded tissue sections from 16 BCC and MCC cases were provided by the Department of Pathology and Laboratory Medicine (Queen’s University), and miRNA FISH was performed in the Laboratory of RNA Molecular Biology (The Rockefeller University). Under blinded conditions, miR-205, miR-375, and rRNA signal intensities were measured on areas with at least 85% tumor composition. miR-205, miR-375, and rRNA signal intensity histograms were obtained to delineate specific RNA and background signals (Supplemental Figure 15). Following background removal, pixel intensities for miR-205, miR-375, and rRNA were multiplied by the corresponding sum of pixels, and these values were subsequently used to normalize miRNA against reference RNA signals. Based on a pilot test of representative tumors (BCC1 and MCC1), a cutoff value of 0.4 was established to differentiate BCC from MCC. Following data collection and interpretation by a blinded tester, sample codes were broken and analyzed.

Quantitative Northern blot analyses. To compare the effects of EDC fixation on miRNA diffusion and retention, we prepared FFPE tissue blocks from macaque brain, obtained 10-μm sections, performed miRNA FISH with and without EDC fixation, recovered total RNA from hybridization buffers and treated tissues, and performed quantitative Northern blot analyses for miR-124 as described previously (16).

Statistics. Statistical analyses were conducted using R language (
http://www.r-project.org/) and contributed packages. A P value of less than 0.05 was considered statistically significant. We tested normality assumptions using the Kolmogorov-Smirnov (KS) test. All P values are 2 tailed unless otherwise specified.

We assessed differences in total miRNA concentrations following different RNA extraction methods using Kruskal-Wallis rank-sum and ANOVA tests; due to the similarity of results only ANOVA test results are presented. Normality assumption was not rejected (KS P = 0.168 and 0.285). When comparing data among BCC, MCC, and NS groups in sequencing run one, pairwise differences were assessed using Tukey’s honest significant difference method.

Differences in miR-205 and miR-375 concentrations and miR-375 real-time PCR values between MCC and non-MCC groups were assessed using ANOVA after log2 transformation. In this scale, the normality assumption was not rejected (P = 0.197, 0.219, and 0.318, respectively). Correlations between miR-205 and miR-375 concentrations were assessed with Pearson correlation for log2-transformed values and Spearman correlation.

Discriminant analyses between MCC and non-MCC groups were performed using linear discriminant analysis (lda package from R). Sequencing run one was used as the training set to construct a classifier, and sequencing run two was used as the testing set to evaluate the performance of the classifier within an independent sample set. Identical results were obtained using the nearest shrunken centroid (pamr package) and support vector machine (svm). The addition of other miRNAs did not improve the performance of the classifier.

Differences in miR-205 and miR-375 fluorescence intensities between BCC and MCC groups were assessed using ANOVA models after log2 transformation. In this scale, the normality assumption was not rejected (P = 0.145, 0.783, and 0.318, respectively).

Study approval. The Rockefeller University Institutional Review Board approved the use of deidentified human FFPE tissues from pathology archives in this study, and informed consent was not required.

N. Renwick is supported through a K08 award (NS072235) from the National Institute of Neurological Disorders and Stroke. M. Hafner is supported by a fellowship from the Charles H. Revson Jr. Foundation. P.S. Moore and Y. Chang are supported as American Cancer Society Professors and through R01 funding from NIH CA136363 and CA120726. T. Tuschl is an HHMI investigator and supported through R01 funding from NIH CA159227 and MH080442 and a grant by the Starr Cancer Consortium. The project described was supported through The Rockefeller University Bridges to Better Medicine Technology Innovation Fund. The project was also partially supported by grant award number (UL1RR024143) from the National Center for Research Resources (NCRR), a component of the NIH and NIH Roadmap for Medical Research, and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. We thank S. Dewell (Genomics Resource Center, The Rockefeller University) for Solexa sequencing, H. Zebroski (Proteomics Core Facility, The Rockefeller University) for peptide synthesis, K. Manova and M. Turkekul (Pathology Core Facility, Memorial Sloan-Kettering Cancer Center) for H&E staining and immunohistochemistry, P. Berninger (EMBL Grenoble) and M. Khorshid (Biozentrum, University of Basel) for additional bioinformatic support, R. Yi (University of Colorado) for wild-type and miR-205 knockout skin tissues, and M. Stoffel (ETH Zürich, Switzerland) for wild-type and miR-375 knockout pancreatic tissue.

Conflict of interest: The methods described herein are the subject of a recent provisional patent application titled “Methods for fixing and detecting RNA” (provisional US patent application no. 61/512,228). Thomas Tuschl is cofounder of Alnylam Pharmaceuticals and is on the scientific advisory board of Regulus Therapeutics.